Annual crop rotation of tropical pastures with no-till soil as affectedby lime surface application.

Soil acidity and low natural fertility are the main limiting factors for grain production in tropical regionssuch as the Brazilian Cerrado. The application of lime to the surface of no-till soil can improve plant nutrition, dry matter production, crop yields and revenue. The present study, conducted at the Lageado Experimental Farm in Botucatu, State of São Paulo, Brazil, is part of an ongoing research project initi-ated in 2002 to evaluate the long-term effects of the surface application of lime on the soil?s chemical attributes, nutrition and kernel/grain yield of peanut (Arachis hypogaea), white oat (Avena sativa L.) and maize (Zea mays L.) inter cropped with palisade grass (Urochloa brizantha cv. Marandu), as well as the forage dry matter yield of palisade grass in winter/spring, its crude protein concentration, estimated meat production, and revenue in a tropical region with a dry winter during four growing seasons. The experiment was designed in randomized blocks with four replications. The treatments consisted of four rates of lime application (0, 1000, 2000 and 4000 kg ha−1), performed in November 2004. The surface application of limestone to the studied tropical no-till soil was efficient in reducing soil acidity from the surface down to a depth of 0.60 m and resulted in greater availability of P and K at the soil surface. Ca and Mg availability in the soil also increased with the lime application rate, up to a depth of 0.60 m. Nutrient absorption was enhanced with liming, especially regarding the nutrient uptake of K, Ca and Mg by plants.Significant increases in the yield components and kernel/grain yields of peanut, white oat and maize were obtained through the surface application of limestone. The lime rates estimated to achieve the maximum grain yield, especially in white oat and maize, were very close to the rates necessary to increase the base saturation of a soil sample collected at a depth of 0?0.20 m to 70%, indicating that the surface liming of 2000 kg ha−1is effective for the studied tropical no-till soil. This lime rate also increases the forage dry matter yield, crude protein concentration and estimated meat production during winter/spring in the maize-palisade grass inter cropping, provides the highest total and mean net profit during the four growing seasons, and can improve the long-term sustainability of tropical agriculture in the Brazilian Cerrado.

Soil N²O emissions from long-term agroecosystems: interactive effects of rainfall seasonality and crop rotation in the Brazilian cerrado.

2016

Expansion of sunflower crop production in Brazil: a survey of future trends.

Sunflower cropped area in Brazil has been showing potential possibilities to be increased in a short period of time for biofuel production. Planning the activities is one of the requirements for the success of future cropped area expansion. This requires a previous survey that identifies future trends in the transformation and rearrangement of the sunflower agro-industry sector and also identifies technological needs that may affect this process. With the objectives of identify future trends and technological needs, a value production chain was built and a questionary was applied to agents of all the sectors participating at the V National Brazilian Symposium of Sunflower and at the XVII Sunflower National Research Meeting Network. The results pointed out a strong tendency for area expansion in the next two to five years (75%); being as a secondary follow-up crop (83%) specially after soybean and top be used for biofuel (77%). The main research needs were linked with disease control, crop zoning and varietal improvement for disease resistance and high oleic oil content. Also considering the vision of and concerns regarding the future expansion and transformation of the sunflower productive complex, it is believed that the expansion is a consolidated trend, requiring a strategic sector planning associated with an economic and technological police for its success within the Brazilian agribusiness.

Agriculture and forest: A sustainable strategy in the Brazilian Amazon.

Large-scale agriculture is increasing in anthropogenically modified areas in the Amazon Basin. Crops such as soybean, maize, oil palm, and others are being introduced to supply the world demand for food and energy. However, the current challenge is to enhance the sustainability of these areas by increasing efficiency of production chains and to improve environmental services. The Amazon Basin has experienced a paradigm shift away from the traditional slash-and-burn agricultural practices, which offers decision makers the opportunity to make innovative interventions to enhance the productivity in previously degraded areas by using trees to ecological advantage. This study describes a successful experiment integrating the production of soybean and paricá (Glycine max L. and Schizolobium amazonicum) based on previous research that indicated potential topoclimatic zones for planting paricá in the Brazilian state of Pará. This paper shows that a no-tillage system reduces the effects of drought compared to conventional tillage still used by many farmers in the region. The integrated system was implemented during the 2014/2015 season in 234.6 ha in the high-potential zone in the municipality of Ulianópolis, Pará. Both soybean and paricá were planted simultaneously. Paricá was planted in 5 m x 2 m inter-tree spacing totaling 228x103 trees per hectare and soybean, in 4 m x 100 m spacing, distributed in nine rows with a 0.45 m inter-row distance, occupying 80% of the area. The harvested soybean production was 3.4 t ha-1, higher than other soybean monocultures in eastern Pará. Paricá benefited from soybean fertilization in the first year: It exhibited rapid development in height (3.26 m) and average diameter (3.85 cm). Trees and crop rotation over the following years is six years for forest species and one year for each crop. Our results confirm there are alternatives to the current production systems able to diminish negative impacts resulting from monoculture. In addition, the system provided environmental services such as reduced soil erosion and increased carbon stock by soil cover with no-tillage soybean cultivation. The soybean cover contributes to increased paricá thermal regulation and lower forestry costs. We concluded that innovative interventions are important to show local farmers that it is possible to adapt an agroforest system to large-scale production, thus changing the Amazon.

Comparison of multitemporal MODIS-EVI smoothing algorithms and its contribution to crop monitoring.

Crop monitoring and more generally land use change detection are of primary importance in order to analyze spatio-temporal dynamics and its impacts on environment. This aspect is especially true in such a region as the State of Mato Grosso (south of the Brazilian Amazon Basin) which hosts an intensive pioneer front. Deforestation in this region as often been explained by soybean expansion in the last three decades. Remote sensing techniques may now represent an efficient and objective manner to quantify how crops expansion really represents a factor of deforestation through crop mapping studies. Due to the special characteristics of the soybean productions' farms in Mato Grosso (area varying between 1000 hectares and 40000 hectares and individual fields often bigger than 100 hectares), the Moderate Resolution Imaging Spectroradiometer (MODIS) data with a near daily temporal resolution and 250 m spatial resolution can be considered as adequate resources to crop mapping. Especially, multitemporal vegetation indices (VI) studies have been currently used to realize this task [1] [2]. In this study, 16-days compositions of EVI (MODQ13 product) data are used. However, although these data are already processed, multitemporal VI profiles still remain noisy due to cloudiness (which is extremely frequent in a tropical region such as south Amazon Basin), sensor problems, errors in atmospheric corrections or BRDF effect. Thus, many works tried to develop algorithms that could smooth the multitemporal VI profiles in order to improve further classification. The goal of this study is to compare and test different smoothing algorithms in order to select the one which satisfies better to the demand which is classifying crop classes. Those classes correspond to 6 different agricultural managements observed in Mato Grosso through an intensive field work which resulted in mapping more than 1000 individual fields. The agricultural managements above mentioned are based on combination of soy, cotton, corn, millet and sorghum crops sowed in single or double crop systems. Due to the difficulty in separating certain classes because of too similar agricultural calendars, the classification will be reduced to 3 classes : Cotton (single crop), Soy and cotton (double crop), soy (single or double crop with corn, millet or sorghum). The classification will use training data obtained in the 2005-2006 harvest and then be tested on the 2006-2007 harvest. In a first step, four smoothing techniques are presented and criticized. Those techniques are Best Index Slope Extraction (BISE) [3], Mean Value Iteration (MVI) [4], Weighted Least Squares (WLS) [5] and Savitzky-Golay Filter (SG) [6] [7]. These techniques are then implemented and visually compared on a few individual pixels so that it allows doing a first selection between the five studied techniques. The WLS and SG techniques are selected according to criteria proposed by [8]. Those criteria are: ability in eliminating frequent noises, conserving the upper values of the VI profiles and keeping the temporality of the profiles. Those selected algorithms are then programmed and applied to the MODIS/TERRA EVI data (16-days composition periods). Tests of separability are realized based on the Jeffries-Matusita distance in order to see if the algorithms managed in improving the potential of differentiation between the classes. Those tests are realized on the overall profile (comprising 23 MODIS images) as well as on each MODIS sub-period of the profile [1]. This last test is a double interest process because it allows comparing the smoothing techniques and also enables to select a set of images which carries more information on the separability between the classes. Those selected dates can then be used to realize a supervised classification. Here three different classifiers are tested to evaluate if the smoothing techniques as a particular effect on the classification depending on the classifiers used. Those classifiers are Maximum Likelihood classifier, Spectral Angle Mapper (SAM) classifier and CHAID Improved Decision tree. It appears through the separability tests on the overall process that the smoothed profiles don't improve efficiently the potential of discrimination between classes when compared with the original data. However, the same tests realized on the MODIS sub-periods show better results obtained with the smoothed algorithms. The results of the classification confirm this first analyze. The Kappa coefficients are always better with the smoothing techniques and the results obtained with the WLS and SG smoothed profiles are nearly equal. However, the results are different depending on the classifier used. The impact of the smoothing algorithms is much better while using the decision tree model. Indeed, it allows a gain of 0.1 in the Kappa coefficient. While using the Maximum Likelihood end SAM models, the gain remains positive but is much lower (Kappa improved of 0.02 only). Thus, this work's aim is to prove the utility in smoothing the VI profiles in order to improve the final results. However, the choice of the smoothing algorithm has to be made considering the original data used and the classifier models used. In that case the Savitzky-Golay filter gave the better results.